Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study

PURPOSE: To study the feasibility of automatically identifying normal digital mammography (DM) exams with artificial intelligence (AI) to reduce the breast cancer screening reading workload. METHODS AND MATERIALS: A total of 2652 DM exams (653 cancer) and interpretations by 101 radiologists were gat...

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Detalles Bibliográficos
Autores principales: Rodriguez-Ruiz, Alejandro, Lång, Kristina, Gubern-Merida, Albert, Teuwen, Jonas, Broeders, Mireille, Gennaro, Gisella, Clauser, Paola, Helbich, Thomas H., Chevalier, Margarita, Mertelmeier, Thomas, Wallis, Matthew G., Andersson, Ingvar, Zackrisson, Sophia, Sechopoulos, Ioannis, Mann, Ritse M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2019
Materias:
Breast
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6682851/
https://www.ncbi.nlm.nih.gov/pubmed/30993432
http://dx.doi.org/10.1007/s00330-019-06186-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6682851/
https://www.ncbi.nlm.nih.gov/pubmed/30993432
http://dx.doi.org/10.1007/s00330-019-06186-9

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